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65 lines
2.0 KiB
65 lines
2.0 KiB
/* Copyright (c) 2016 Baidu, Inc. All Rights Reserve.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License. */
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#include "paddle/utils/Logging.h"
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#include <fstream>
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#include "AverageOptimizer.h"
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#include "FirstOrderOptimizer.h"
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#include "OptimizerFunctions.h"
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#include "OptimizerWithRegularizer.h"
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#include "ParameterOptimizer.h"
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#include "hl_gpu.h"
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namespace paddle {
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ParameterOptimizer* ParameterOptimizer::create(
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const OptimizationConfig& optConfig, bool inPserver) {
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if (inPserver && optConfig.num_batches_per_send_parameter() > 1) {
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return new AddOptimizer(optConfig);
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}
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if (optConfig.learning_method() == "momentum") {
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return new SgdOptimizer(optConfig);
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}
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if (optConfig.learning_method() == "torch_momentum") {
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return new SgdOptimizer(optConfig);
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}
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if (optConfig.learning_method() == "adagrad") {
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return new AdagradParameterOptimizer(optConfig);
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}
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if (optConfig.learning_method() == "adadelta") {
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return new AdaDeltaParameterOptimizer(optConfig);
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}
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if (optConfig.learning_method() == "rmsprop") {
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return new RMSPropParameterOptimizer(optConfig);
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}
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if (optConfig.learning_method() == "decayed_adagrad") {
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return new DecayedAdagradParameterOptimizer(optConfig);
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}
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if (optConfig.learning_method() == "adam") {
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return new AdamParameterOptimizer(optConfig);
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}
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if (optConfig.learning_method() == "adamax") {
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return new AdamaxParameterOptimizer(optConfig);
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}
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if (optConfig.learning_method() == "sparse_momentum") {
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return new SparseMomentumParameterOptimizer(optConfig);
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}
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return nullptr;
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}
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} // namespace paddle
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